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from typing import Dict, List, Any
from transformers import CLIPTokenizer, CLIPModel
import numpy as np
class EndpointHandler:
def __init__(self, path=""):
# load the model
repo_id = "openai/clip-vit-large-patch14-336"
self.model = CLIPModel.from_pretrained(repo_id)
self.tokenizer = CLIPTokenizer.from_pretrained(repo_id)
def __call__(self, data: Dict[str, Any]) -> List[float]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# compute the embedding of the input
query = data["inputs"]
inputs = self.tokenizer(query, padding=True, return_tensors="pt")
text_features = self.model.get_text_features(**inputs)
text_features = text_features.detach().numpy()
input_embedding = text_features[0]
# normalize the embedding
input_embedding = input_embedding / np.linalg.norm(input_embedding)
return input_embedding.tolist()
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